Hierarchical structural component model for pathway analysis of common variants

Abstract

Background: Genome-wide association studies (GWAS) have been widely used to identify phenotype-related genetic variants using many statistical methods, such as logistic and linear regression. However, GWAS-identified SNPs, as identified with stringent statistical significance, explain just a small portion of the overall estimated genetic heritability. To address this 'missing heritability' issue, gene-and pathway-based analysis, and biological mechanisms, have been used for many GWAS studies. However, many of these methods often neglect the correlation between genes and between pathways. Methods: We constructed a hierarchical component model that considers correlations both between genes and between pathways. Based on this model, we propose a novel pathway analysis method for GWAS datasets, Hierarchical structural Component Model for Pathway analysis of Common vAriants (HisCoM-PCA). HisCoM-PCA first summarizes the common variants of each gene, first at the gene-level, and then analyzes all pathways simultaneously by ridge-type penalization of both the gene and pathway effects on the phenotype. Statistical significance of the gene and pathway coefficients can be examined by permutation tests. Results: Using the simulation data set of Genetic Analysis Workshop 17 (GAW17), for both binary and continuous phenotypes, we showed that HisCoM-PCA well-controlled type I error, and had a higher empirical power compared to several other methods. In addition, we applied our method to a SNP chip dataset of KARE for four human physiologic traits: (1) type 2 diabetes; (2) hypertension; (3) systolic blood pressure; and (4) diastolic blood pressure. Those results showed that HisCoM-PCA could successfully identify signal pathways with superior statistical and biological significance. Conclusions: Our approach has the advantage of providing an intuitive biological interpretation for associations between common variants and phenotypes, via pathway information, potentially addressing the missing heritability conundrum.

Genome-wide Association Study of Caffeine Consumption Using Coriell Personalized Medicine Collaborative Data

“…These new and replication findings add support to a growing body of literature characterizing the suggestive influence of genetics on caffeine consumption habits that, particularly when combined with a better functional metabolic and health outcome understanding, has the potential to inform lifestyle choices about consumption behavior. Further research with larger cohorts into the role of genetics in mediating the relationship between coffee and caffeine consumption and health status is of merit [81,82]. Such work has the potential to eventually inform personalized interventions aimed at improving cardiovascular, metabolic, and quality of life outcomes that may be linked to common consumption habits.…”

Pathway-Based Integrative Analysis of Metabolome and Microbiome Data from Hepatocellular Carcinoma and Liver Cirrhosis Patients

“…PHARAOH is a pathway analysis method for rare genetic variants that analyzes pathways using a single hierarchical model consisting of collapsed gene-level summaries and pathways. This method was later extended to various data types such as common genetic variants, and miRNA and mRNA expression [ 18 , 19 , 20 , 21 , 22 ].…”

HisCoM-PCA: software for hierarchical structural component analysis for pathway analysis based using principal component analysis

“…Recently, we presented a hierarchical structural component model (HisCoM) for pathway analysis of common variants (HisCoM-PCA) to identify pathways associated with traits [5]. HisCoM-PCA is based on principal component analysis (PCA) for dimension reduction of SNPs in each gene, and the HisCoM for pathway analysis.…”